Integrating Enhanced Security Protocols with Moving Object Detection: A Yolo-Based Approach for Real-Time Surveillance

This paper presents a novel approach to enhancing real-time surveillance systems using an augmented YOLO (You Only Look Once) v8 architecture. The primary focus is on integrating advanced security features into the YOLO framework to improve the detection and analysis of moving objects in various surveillance scenarios. The key enhancements include the implementation of an Anomaly Detection Layer, the integration of Behavior Analysis Algorithms, and the enhancement of Real-Time Data Processing capabilities. These modifications are designed to increase the accuracy of threat detection while maintaining the high processing speeds essential for real-time applications. The Anomaly Detection Layer utilizes unsupervised learning algorithms to identify deviations from normal patterns, enabling the system to flag potential security threats that might otherwise go unnoticed. The Behavior Analysis Algorithms are incorporated to analyze movement patterns and identify suspicious behaviors, adding a layer of contextual understanding to object detection. To ensure the practicality of these enhancements, the model is optimized to handle live video feeds with minimal latency, maintaining an average processing speed of 45 frames per second. The experimental setup involved testing the enhanced YOLO v8 model in various simulated and real-world environments, comparing its performance with standard YOLO v8 and other existing surveillance models. The results demonstrated a significant improvement in detection accuracy, particularly in identifying anomalous behaviors and potential threats. The enhanced model outperformed the baseline models in various complex scenarios, including crowded public spaces and high-activity zones. This research contributes to the field of surveillance technology by offering a more sophisticated and efficient tool for public safety and security applications. It highlights the potential of integrating advanced AI features into existing object detection frameworks to create more intelligent and responsive surveillance systems. Future work will focus on further optimizing the model, addressing ethical and privacy concerns, and exploring broader applications in other real-time analysis domains.